@inbook{0dc56dd838164ce89fd27dca479277d6,
title = "OpenTox Principles and Best Practices for Trusted Reproducible In Silico Methods Supporting Research and Regulatory Applications in Toxicological Science",
abstract = "Our aim in this work and initiative is to establish a practice and guidance for tracking and reporting modern in silico data analyses in a reproducible manner. The recommended reproducible principle supports the concept that data analyses, and more generally, scientific claims and regulatory evidence, are published with their raw data and software code so that others may verify the findings and build upon them. We discuss here how we are demonstrating implementations of trusted reproducible in silico evidence workflows and are enhancing their acceptance with an open knowledge community approach supported within OpenTox and OpenRiskNet. The general principle discussed in this article can be applied in regulatory settings.",
author = "Barry Hardy and Daniel Bachler and Joh Dokler and Thomas Exner and Connor Hardy and Weida Tong and Daniel Burgwinkel and Richard Bergstr{\"o}m",
year = "2019",
language = "English",
isbn = "978-3-030-16442-3",
series = "Challenges and Advances in Computational Chemistry and Physics",
publisher = "Springer",
pages = "383--403",
booktitle = "Advances in Computational Toxicology",
}